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Liu, Y., Zhu, X., Wei, Z., Wu, K., Zhang, J., Mutti, F. G., Zhang, H., Loeffler, F. F., & Zhou, J. (2023). Multi-Channel Lanthanide Nanocomposites for Customized Synergistic Treatment of Orthotopic Multi-Tumor Cases. Angewandte Chemie, 135(30), Article e202303570. https://doi.org/10.1002/ange.202303570, https://doi.org/10.1002/anie.202303570 -
van Beurden, A. W., Meylahn, J. M., Achterhof, S., Buijink, R., Olde Engberink, A., Michel, S., Meijer, J. H., & Rohling, J. H. T. (2023). Reduced Plasticity in Coupling Strength in the Aging SCN Clock as Revealed by Kuramoto Modeling. Journal of biological rhythms, 38(5), 461-475. https://doi.org/10.1177/07487304231175191 -
Deblais, A., Prathyusha, K. R., Sinaasappel, R., Tuazon, H., Tiwari, I., Patil, V. P., & Bhamla, M. S. (2023). Worm blobs as entangled living polymers: From topological active matter to flexible soft robot collectives. Soft Matter, 19(37), 7057-7069. https://doi.org/10.1039/d3sm00542a -
Braunsteins, P., den Hollander, F., & Mandjes, M. (2023). A sample-path large deviation principle for dynamic Erdős–Rényi random graphs. The Annals of Applied Probability, 33(4), 3278-3320. https://doi.org/10.1214/22-AAP1892 -
Coulombe, L.-P., Shivkumar, H., Désert, J.-M., & NASA’s Nexus for Exoplanet System Science (2023). A broadband thermal emission spectrum of the ultra-hot Jupiter WASP-18b. Nature, 620(7973), 292-298. https://doi.org/10.1038/s41586-023-06230-1 -
Dotsenko, V., Shadrin, S., & Tamaroff, P. (2023). Generalized Cohomological Field Theories in the Higher Order Formalism. Communications in Mathematical Physics, 399(3), 1439-1500. https://doi.org/10.48550/arXiv.2112.06015, https://doi.org/10.1007/s00220-022-04577-6 -
Buitendijk, N. H., & Nolet, B. A. (2023). Timing and intensity of goose grazing: Implications for grass height and first harvest. Agriculture, Ecosystems and Environment, 357, Article 108681. https://doi.org/10.1016/j.agee.2023.108681 -
Mücke, N. T., Pandey, P., Jain, S., Bohté, S. M., & Oosterlee, C. W. (2023). A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning. Sensors, 23(13), Article 6179. https://doi.org/10.3390/s23136179
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